# How to integrate Process street MCP with CrewAI

```json
{
  "title": "How to integrate Process street MCP with CrewAI",
  "toolkit": "Process street",
  "toolkit_slug": "process_street",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/process_street/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/process_street/framework/crew-ai.md",
  "updated_at": "2026-05-06T08:24:34.913Z"
}
```

## Introduction

This guide walks you through connecting Process street to CrewAI using the Composio tool router. By the end, you'll have a working Process street agent that can start a new onboarding checklist for a new hire, find all workflow runs due this week, list all available sop templates in your account through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Process street account through Composio's Process street MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Process street with

- [OpenAI Agents SDK](https://composio.dev/toolkits/process_street/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/process_street/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/process_street/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/process_street/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/process_street/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/process_street/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/process_street/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/process_street/framework/cli)
- [Google ADK](https://composio.dev/toolkits/process_street/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/process_street/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/process_street/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/process_street/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/process_street/framework/llama-index)

## TL;DR

Here's what you'll learn:
- Get a Composio API key and configure your Process street connection
- Set up CrewAI with an MCP enabled agent
- Create a Tool Router session or standalone MCP server for Process street
- Build a conversational loop where your agent can execute Process street operations

## What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.
Key features include:
- Agent Roles: Define specialized agents with specific goals and backstories
- Task Management: Create tasks with clear descriptions and expected outputs
- Crew Orchestration: Combine agents and tasks into collaborative workflows
- MCP Integration: Connect to external tools through Model Context Protocol

## What is the Process street MCP server, and what's possible with it?

The Process street MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Process Street account. It provides structured and secure access to your workflows, so your agent can perform actions like listing workflows, launching new workflow runs, searching data sets, completing processes, and recovering deleted runs on your behalf.
- Automated workflow management: Let your agent retrieve all available workflows and help you navigate or select the right process for any task.
- Instant workflow run creation: Have your AI assistant spin up new workflow runs from templates, name them, set due dates, and generate share links as needed.
- Targeted data set search: Ask your agent to search and filter rows within Process Street data sets to quickly find matching entries based on your criteria.
- Workflow run completion: Direct your agent to mark entire workflow runs as completed, ensuring processes are finished and statuses are up to date.
- One-click workflow run recovery: Quickly restore accidentally deleted workflow runs, letting your agent correct mistakes and recover lost progress with ease.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `PROCESS_STREET_COMPLETE_WORKFLOW_RUN` | Complete a workflow run | This tool marks an entire workflow run as completed in process street. it updates the workflow run's status to 'completed', distinguishing it from process street complete task which completes individual tasks. |
| `PROCESS_STREET_CREATE_WORKFLOW_RUN` | Create Workflow Run | This tool creates a new workflow run from a specified workflow template. it is one of the most fundamental operations in process street, allowing users to initiate a new instance of a workflow. the tool requires a workflow template id and optionally allows setting a custom name, due date, and whether to enable a share link. |
| `PROCESS_STREET_FIND_DATA_SET_ROWS` | Find Data Set Rows | This tool allows you to search for records within a data set based on form fields. it's useful for retrieving specific records from a data set when you need to find matching entries based on certain criteria. |
| `PROCESS_STREET_LIST_WORKFLOWS` | List Workflows | This tool retrieves a list of all workflows available in the process street account. it is a fundamental action that allows users to view and access all their workflows, which is essential for other operations that require workflow ids. this action is important because it provides the foundation for other actions that require workflow ids as input parameters, such as creating workflow runs or managing workflow-specific tasks, thereby enabling better workflow management and automation. |
| `PROCESS_STREET_UNDELETE_WORKFLOW_RUN` | Undelete Workflow Run | This tool allows you to restore a previously deleted workflow run in process street. it uses the put /v1.1/workflow-runs/{workflowrunid}/undelete endpoint to recover a workflow run within a valid recovery period. it complements the existing process street delete workflow run action by providing a data recovery option to correct deletion mistakes. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Process street MCP server is an implementation of the Model Context Protocol that connects your AI agent to Process street. It provides structured and secure access so your agent can perform Process street operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before starting, make sure you have:
- Python 3.9 or higher
- A Composio account and API key
- A Process street connection authorized in Composio
- An OpenAI API key for the CrewAI LLM
- Basic familiarity with Python

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider.
- Keep the API key safe.
Composio API Key
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

**What's happening:**
- composio connects your agent to Process street via MCP
- crewai provides Agent, Task, Crew, and LLM primitives
- crewai-tools[mcp] includes MCP helpers
- python-dotenv loads environment variables from .env
```bash
pip install composio crewai crewai-tools[mcp] python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates with Composio
- USER_ID scopes the session to your account
- OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

**What's happening:**
- CrewAI classes define agents and tasks, and run the workflow
- MCPServerHTTP connects the agent to an MCP endpoint
- Composio will give you a short lived Process street MCP URL
```python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
```

### 5. Create a Composio Tool Router session for Process street

**What's happening:**
- You create a Process street only session through Composio
- Composio returns an MCP HTTP URL that exposes Process street tools
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["process_street"])

url = session.mcp.url
```

### 6. Initialize the MCP Server

**What's Happening:**
- Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
- MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
- Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
- Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
- Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
```python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
```

### 7. Create a CLI Chatloop and define the Crew

**What's Happening:**
- Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
- Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
- Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
- Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
- Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
- Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.
```python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
```

## Complete Code

```python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["process_street"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")
```

## Conclusion

You now have a CrewAI agent connected to Process street through Composio's Tool Router. The agent can perform Process street operations through natural language commands.
Next steps:
- Add role-specific instructions to customize agent behavior
- Plug in more toolkits for multi-app workflows
- Chain tasks for complex multi-step operations

## How to build Process street MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/process_street/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/process_street/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/process_street/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/process_street/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/process_street/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/process_street/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/process_street/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/process_street/framework/cli)
- [Google ADK](https://composio.dev/toolkits/process_street/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/process_street/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/process_street/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/process_street/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/process_street/framework/llama-index)

## Related Toolkits

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- [Fillout forms](https://composio.dev/toolkits/fillout_forms) - Fillout forms is an online platform for building and managing forms with a flexible API. It lets you create, distribute, and collect responses from forms with ease.
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- [Formsite](https://composio.dev/toolkits/formsite) - Formsite lets you build online forms and surveys with drag-and-drop simplicity. Capture, manage, and integrate form responses securely for streamlined workflows.
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- [Hyperbrowser](https://composio.dev/toolkits/hyperbrowser) - Hyperbrowser is a next-generation platform for scalable browser automation. It empowers AI agents to interact with web apps, automate workflows, and handle browser sessions at scale.
- [La Growth Machine](https://composio.dev/toolkits/lagrowthmachine) - La Growth Machine automates multi-channel sales outreach and routine tasks for sales teams. Streamline your workflow and focus on closing more deals.
- [Leverly](https://composio.dev/toolkits/leverly) - Leverly is a workflow automation platform that connects and coordinates actions across your apps. It streamlines repetitive processes so your business runs smoother, faster, and with fewer manual steps.
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- [Make](https://composio.dev/toolkits/make) - Make is an automation platform that connects your favorite apps and services. Build powerful, custom workflows without writing code.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Process street MCP?

With a standalone Process street MCP server, the agents and LLMs can only access a fixed set of Process street tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Process street and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Process street tools.

### Can I manage the permissions and scopes for Process street while using Tool Router?

Yes, absolutely. You can configure which Process street scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Process street data and credentials are handled as safely as possible.

---
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